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Open research questions in Computational and Text Analysis Methods

254 unresolved questions extracted from the limitations and future-work sections of 927 Computational and Text Analysis Methods papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Consequently, future research should investigate annotation elicitation methods that allow participants to convey the important aspects of the submissions coherently and concisely in a repeatable fashion while facilitating scalable analysis. Large dataset analysis and insight extraction with adequate steering control per user goals is an open problem, documented previously [24]. While PLACES provides a list of concrete failure examples, it also poses an open question for model developers and safety researchers about the adequate model response in such situations.

    Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South · 2026

  • further studies should examine the relationship between metaknowledge and modeling practices in different contexts, - future research should investigate the role of content knowledge in the use of modeling practices, - the study suggests that examining the interplay between meta and content knowledge in modeling practices is a promising area for future research

    Relating Metaknowledge and Modeling Practices in Connection to Content Knowledge about the Phenomenon · 2026 · DOI

  • further investigation of the differences between LLMs and human behavior, - exploration of the applications of the Bayesian framework in other domains, - examination of the effects of fine-tuning on LLMs in different contexts

    Disentangling interaction and bias effects in opinion dynamics of large language models · 2026 · DOI

  • The lack of a systematic framework to quantify and compare interaction and bias effects in LLM opinion dynamics. The need to disentangle genuine interaction from systematic biases in large language models. The limited understanding of how biases affect opinion dynamics in large language models.

    Disentangling interaction and bias effects in opinion dynamics of large language models · 2026 · DOI

  • Yet this assumption is rarely studied directly, leaving us with scant knowledge about what people actually learn when accessing information about norms like human rights.

    The Language Barrier to Human Rights Information: A Global Analysis of Google Search Results · 2026 · DOI

  • Indeed, delegating judgment to such systems may affect the heuristics underlying evaluative processes, suggesting a shift from normative reasoning toward pattern-based approximation and raising open questions about the role of LLMs in evaluative processes.

    The simulation of judgment in LLMs · 2025 · DOI

  • Moreover, this work also considers other underexplored dangers of AI development for the environment and, hypothetically, for sentient AI.

    Focal points and blind spots of human-centered AI: AI risks in written online media · 2025 · DOI

  • Future research could explore the use of larger and more complex generative models. Future research could investigate the effects of different training data on model outputs. Future research could develop more sophisticated methods for sentiment analysis and framing detection.

    Training Generative AI on Social Media Data: Implications and Outputs - A Worked Out Example · 2026 · DOI

  • The gap between how generative AI models are developed and what researchers can study. The lack of transparency and cautious interpretation in AI research.

    Training Generative AI on Social Media Data: Implications and Outputs - A Worked Out Example · 2026 · DOI

  • Applying the UMS to other historical moments and events. Refining the UMS protocol to improve its effectiveness and efficiency. Exploring the potential applications of the UMS in other fields, such as social sciences and natural sciences.

    The Undirected Multidisciplinary Sweep: A General Method for Detecting Structural Blindness in Historical Records · 2026 · DOI

  • The gap between what is logically obvious and what is operationally executed in historical research. The lack of a systematic approach to detecting structural blindness in historical records.

    The Undirected Multidisciplinary Sweep: A General Method for Detecting Structural Blindness in Historical Records · 2026 · DOI

  • The study only evaluates five Large Language Models. The corpus is limited to Indian, Singaporean, and Nigerian English varieties. The study does not investigate the impact of cultural ghosting on human subjects.

    When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models · 2026 · DOI

  • Investigating the impact of cultural ghosting on human subjects. Developing strategies for culturally-aware alignment. Evaluating the effectiveness of cultural-preservation prompts in different cultural contexts.

    When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models · 2026 · DOI

  • Cultural biases in LLMs. Limited ability of existing evaluation frameworks to capture cultural inclusivity. The need for a benchmark to evaluate Global Cultural Inclusivity in LLMs.

    WorldView-Bench: A Benchmark for Evaluating Global Cultural Perspectives in Large Language Models · 2026 · DOI

  • Existing benchmarking frameworks fail to adequately capture cultural bias. The lack of a benchmark for evaluating Global Cultural Inclusivity in LLMs.

    WorldView-Bench: A Benchmark for Evaluating Global Cultural Perspectives in Large Language Models · 2026 · DOI

  • Future research should focus on integrating thematic areas and strengthening research alliances. Future research should examine the application of AI in crisis management and the development of responsible AI frameworks.

    Mapping the Nexus: A Bibliometric Analysis of the Application of Artificial Intelligence (AI) in Polycrisis Research (2019 – 2026) · 2026 · DOI

  • The research area of polycrisis is fragmented, with no systematic mapping of the role of AI. The study identifies a gap in the integration of thematic areas and the development of responsible AI frameworks.

    Mapping the Nexus: A Bibliometric Analysis of the Application of Artificial Intelligence (AI) in Polycrisis Research (2019 – 2026) · 2026 · DOI

  • Longitudinal empirical studies are needed to further explore the integration of deep learning and digital literacy. Inclusive AI-driven pedagogical designs should be developed and evaluated. Further research is needed to address the research gap in using AI for fundamental research processes.

    Deep Learning and Digital Literacy: A Systematic Literature Network and Bibliometric Review · 2026 · DOI

  • A significant research gap exists in using AI for fundamental research processes compared to its dominance in instructional assessment. The study identifies a need for further research on the application of deep learning in digital literacy.

    Deep Learning and Digital Literacy: A Systematic Literature Network and Bibliometric Review · 2026 · DOI

  • Topic modelling is often criticised for being overly descriptive and lacking theoretical integration. The study acknowledges the limitations of traditional topic modelling, including reliance on oversimplified assumptions and limited theoretical integration. The study also acknowledges the challenges related to reliability, validity, and multilingual inclusivity.

    Going beyond description: multilingual topic modelling and theoretical integration in comparative media analysis · 2026 · DOI

  • The analysis of actual media content across nations has received comparatively less attention and has relied largely on manual methods. There is a need for more effective methods for analysing large-scale textual datasets in comparative media analysis. The study aims to address the limitations of traditional topic modelling and contribute to the development of more effective methods for comparative media analysis.

    Going beyond description: multilingual topic modelling and theoretical integration in comparative media analysis · 2026 · DOI

  • The information gap between medical institutions and vaccine-hesitant populations. The lack of effective public health communication strategies to address individual queries and monitor population-level discourse.

    Japanese-Language AI Agent System for Human Papillomavirus Vaccine Infoveillance and Public Communication: Development and Feasibility Evaluation · 2026 · DOI

  • Future research should investigate the causes of bias in LLMs and develop solutions to mitigate bias. Future research should examine the implications of bias in LLMs for various applications. Future research should develop more comprehensive and nuanced methods for evaluating bias in LLMs.

    Measuring stereotype and deviation biases in large language models · 2026 · DOI

  • There is a lack of understanding of the limitations and potential risks of LLMs, particularly in regards to bias. There is a need to investigate the causes of bias in LLMs and to develop solutions to mitigate bias.

    Measuring stereotype and deviation biases in large language models · 2026 · DOI

  • The treatment-timing rule is mechanically related to output. The event-study comparison is asymmetric in a way that mimics a causal effect. The design problem identified in this paper is a challenge for future research.

    Comment on Scientific production in the era of large language models · 2026

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254 open questions have been extracted from the limitations and future-work passages of 927 Computational and Text Analysis Methods papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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